Center for Neural Science
Multi-scale theory of neural representations in biological and artificial neural networks
A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of individual neurons and networks of such neurons. To achieve this, it is crucial to bridge phenomena across multiple scales, from the microscopic responses of individual neurons to the emergent macroscopic phenomena of cognitive and task functions. The structures of neuron population activities manifest themselves as neural representations. Neural computation can be viewed as a series of transformations of these representations through various processing stages of the brain. Therefore, theories that help us comprehend the structure and transformation of these representations illuminate the ‘black box’ of computation in both biological and artificial neural networks. The primary focus of my lab’s research is to develop such theories of neural representations that describe the principles of neural coding and, importantly, capture the complex structure of real data from both biological and artificial systems. To achieve this, we construct theories, methods, and models that capture normative principles connecting multiple scales, using methods in statistical physics, machine learning, high-dimensional statistics, and geometry.
In this talk, I will discuss three related approaches towards this direction: First, we develop new theories of neural representations to connect the structure of neural population activities to the underlying computational processes. Second, we employ these theories to analyze how these representations evolve across scales, shaped by the properties of single neurons and the transformations across distinct brain regions. Finally, we use both experimental and theoretical insights from neuroscience as a design principle for developing new models of the brain, with a particular focus on artificial neural networks using deep learning techniques. By expanding our mathematical toolkit for analyzing representations underlying complex neuronal networks, we hope to contribute to the long-term challenge of understanding the neuronal basis of tasks and behaviors.
Speaker bio: SueYeon Chung is an Assistant Professor in the Center for Neural Science at NYU, with a joint appointment in the Center for Computational Neuroscience at the Flatiron Institute, an internal research division of the Simons Foundation. She is also an affiliated faculty member at the Center for Data Science and Cognition and Perception Program at NYU. Prior to joining NYU, she was a Postdoctoral Fellow in the Center for Theoretical Neuroscience at Columbia University, and BCS Fellow in Computation at MIT. Before that, she received a Ph.D. in applied physics at Harvard University, and a B.A. in mathematics and physics at Cornell University. She is the recipient of the Klingenstein-Simons Fellowship Award in Neuroscience in 2023, and the Alfred P. Sloan Fellowship Award in 2024. Her main research interests lie at the intersection between computational neuroscience and deep learning, with a particular focus on understanding and interpreting neural computation in biological and artificial neural networks by employing methods from neural network theory, statistical physics, and high-dimensional statistics.
A pizza lunch willl be served.